RL methods adhere to a well-defined interface with any possible environment (the agent acts on the environment which in turn steps to the next state after providing a reward on the chosen action). Based on this interface, an open-source framework called Gym [1]} has been developed to facilitate research of new RL algorithmic approaches on various environments, such as bipedal robot walking (Humanoid-v2) and playing a specific video game (Pong-ram-v0) to name a few. As a major benefit, Gym allows the implementation of custom environments for new problems where RL methods could be found useful to address them. Similarly, we have extendedThe tool is available to download on the workshop's webpage: https://sites.google.com/view/mlarchsys/isca-2021. Gym by defining several custom BP environments (gym_bp_cbp2016) based on the public traces of CBP-5 to explore new RL policies on branch prediction.
